Introduction to Multilayer Perceptrons (MLPs)

Multilayer Perceptrons (MLPs) are the foundation of deep learning. They consist of multiple layers of interconnected neurons that can model complex nonlinear relationships between inputs and outputs. MLPs belong to the family of feedforward neural networks.

📘 Structure of MLP:

• Input Layer: Receives raw input data such as features or pixel values.
• Hidden Layers: Extract abstract features using activation functions.
• Output Layer: Produces the final output such as classification label or regression value.

🧮 Mathematical Representation:

Y = f(Σ(Wi * Xi) + b)
where:
Wi = weights,
Xi = inputs,
b = bias,
f = activation function.

⚙️ Common Activation Functions:

• Sigmoid: Smooth curve between 0 and 1, used for binary outputs.
• Tanh: Maps values between -1 and 1.
• ReLU: Most widely used, helps prevent vanishing gradients.

🧠 Working Process:

1. Input data passes through weighted connections.
2. Each neuron applies an activation function to produce an output.
3. The output propagates forward until the final prediction.
4. Backpropagation updates the weights to minimize the loss function.

📈 Applications of MLP:

• Image and speech recognition
• Financial forecasting
• Natural language processing
• Predictive analytics

📗 Advantages & Limitations:

• ✅ Capable of learning complex nonlinear relationships.
• ✅ Adaptable to various data types and domains.
• ⚠️ Requires large computational power and data.
• ⚠️ Can overfit if not regularized properly.

📘 Conclusion:

MLPs are the backbone of deep learning. They inspire advanced architectures like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. Understanding MLPs is essential for mastering modern AI systems.